Thinking about platforming with more traditional mediatization: Lessons from audiovisual analysis
Bibliographic record
Abstract
Since the mid-1990s, film and television industries are more and more confronted with the appearance of new intermediation services which have created platforms. In a project funded by the Social Sciences and Humanities Research Council of Canada (SSHRC), we try to analyze the place and role that these new services are taking in the audiovisual sector. Our corpus is composed of the platforms of four companies that have developed activities on a vast international scale, Netflix (with its service of the same name), Amazon (Prime Video service), Disney (Disney+) and Apple (Apple TV+). Based on our corpus, it seems to us that some changes have been the result of firms’ activities, but that it is not as linear as it may appear at first sight. Transformations are at work but there is also some “Old Media Persistence.” Thus, we find a certain “contamination” of old practices originating from the organization of industrial channels and forms in the mutations currently presented by these new intermediation services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".